Rajesh Gopakumar

Bio: Rajesh Gopakumar is an academic researcher from Manipal Institute of Technology. The author has contributed to research in topics: Image segmentation & Optical character recognition. The author has an hindex of 2, co-authored 9 publications receiving 30 citations. Previous affiliations of Rajesh Gopakumar include Manipal University.

Zone-based structural feature extraction for script identification from Indian documents

Abstract: Automatic identification of a script in a given document image facilitates many important applications such as automatic archiving of multilingual documents, searching online archives of document images and for the selection of script specific OCR in a multilingual environment. In this paper a Zone-based Structural feature extraction algorithm scheme towards the recognition of South-Indian scripts along with English and Hindi is proposed. The document images are segmented into lines and the line image is divided into different zones and the structural features are extracted. A total of 37 features were extracted in the first level and then reduced to an optimal number of features using wrapper and filter selection approaches. The K-nearest neighbor and the support vector machine classifiers are used for classification and recognition purpose. A classification accuracy of 100% is achieved on the optimal feature set.

Script Identification from Multilingual Indian Documents using Structural Features

Abstract: Script Identification from a given document image is an important process for many computer applications such as automatic archiving of multilingual documents, searching online archives of document images and for the selection of script specific OCR in a multilingual environment. In this paper a Zone-based Structural feature extraction algorithm towards the recognition of South-Indian scripts along with English and Hindi is proposed. The document images are segmented into lines and the line image is divided into different zones and the structural features are extracted. A total of 37 features were extracted in the first level and then reduced to an optimal number of features using wrapper and filter selection approaches. The K-nearest neighbor and the support vector machine classifiers are used for classification and recognition purpose. Very good classification accuracy is achieved on the optimal feature set

Multi-Script Line Identification System for Indian Languages

Abstract: India is a multilingual multi-script country. There are totally 18 official languages and 12 scripts in India. For Optical Character Recognition (OCR) of such a multi-lingual document, it is necessary to identify the script before feeding the text lines to the OCRs of individual scripts. In this paper, a simple and efficient technique of script identification for Kannada, Malayalam, Telugu, Tamil, Gujarati, Hindi and English text lines from a printed document is presented. The proposed system uses horizontal projection profile, Vertical projection profile and Top pitch information to distinguish the seven scripts. The knowledge base of the system is developed based on 50 different document images containing about 250 text lines of each script. The proposed system is tested on 50 different document images containing about 250 text lines of each script and an overall classification rate of 97.64% is achieved.

Detecting the edge of multiple images in parallel

Abstract: Edge is variation of brightness in an image. Edge detection is useful in many application areas such as finding forests, rivers from a satellite image, detecting broken bone in a medical image etc. The paper discusses about finding edge of multiple aerial images in parallel. The proposed work tested on 38 images 37 colored and one monochrome image. The time taken to process N images in parallel is equivalent to time taken to process 1 image in sequential. Message Passing Interface (MPI) and Open Computing Language (OpenCL) is used to achieve task and pixel level parallelism respectively.

A Review of Application of Computer-vision for Quality Grading of Food Products

Abstract: Quality grading is necessary for making any food product marketable. Quality parameters of food products include both Visual and Olfactory factors. The paper attempts to discuss studies conducted in the field of Computer Vision that in-turn uses Digital Image processing and Soft-computing tools in the field of Food Processing Industry. The outcome of this paper will help in understanding the application of Computer Vision in automating the inspection of food products based on various well define Quality parameters.

Human Memory Update Strategy: A Multi-Layer Template Update Mechanism for Remote Visual Monitoring

Abstract: In the era of rapid development of artificial intelligence, the integration of multimedia and human-artificial intelligence has become an important research hotspot. Especially in the multimedia environment, effective remote visual monitoring has become the exploration direction of many scholars. The use of traditional correlation filtering (CF) algorithm for real-time monitoring in the context of multimedia is a practical strategy. However, most existing filtering-based visual monitoring algorithms still have the problem of insufficient robustness and effectiveness. Therefore, by considering the strategy of updating human memory, this paper proposes a multi-layer template update mechanism to achieve effective monitoring in a multimedia environment. In this strategy, the weighted template of the high-confidence matching memory is used as the confidence memory, and the unweighted template of the low-confidence matching memory is used as the cognitive memory. Through the alternate use of confidence memory, matching memory, and cognitive memory, it is ensured that the target will not be lost during the monitoring process. Experimental results show that this strategy does not affect the speed (still real-time) and improves the robustness in the multimedia background.

Script identification algorithms: a survey

Abstract: Script identification is being widely accepted techniques for selection of the particular script OCR (Optical Character Recognition) in multilingual document images. Extensive research has been done in this field, but still it suffers from low identification accuracy. This is due to the presence of faded document images, illuminations and positions while scanning. Noise is also a major obstacle in the script identification process. However, it can only be minimized up to a level, but cannot be removed completely. In this paper, an attempt is made to analyze and classify various script identification schemes for document images. The comparison is also made between these schemes, and discussion is made based upon their merits and demerits on a common platform. This will help the researchers to understand the complexity of the issue and identify possible directions for research in this field.

Identification of Kashmiri script in a bilingual document image

Abstract: Script identification is a very important field in the area of pattern recognition & document image analysis. Commendable work has been proposed and implemented to recognize various common scripts in unilingual, bilingual and multilingual contexts. So far, diminutive work has been presented for Kashmiri script identification. In this paper, we are describing and experimentally testing our approach for identification of Kashmiri script with respect to English script which comprises a text document image. Two important and simple features are used for identification of scripts: Horizontal Profile Coefficients (Peaks) & Horizontal Profile Valleys.